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This research paper introduces an optimization methodology for the strategic electric sources’ placement at multiple positions in a DC islanded microgrid characterized by a mesh network, aiming to minimize line losses while considering minimal cable weight. The DC microgrid studied in this paper is composed of PV panels, batteries, a diesel generator, and 20 residential loads. Employing Dijkstra’s algorithm, a graph algorithm used in Google Maps, the study identifies the shortest path (resistance) between potential source nodes and various variable loads within a predefined electric distribution mesh network topology. This study focuses on active power considerations and offers valuable insights into the placement optimization of multiple sources’ positions in DC microgrid mesh networks. A key contribution of this paper lies in the ranking of source node positions based on minimal to maximal line losses, taking into consideration optimal cable weights, while using MATPOWER to validate sources’ ranking based on Dijkstra’s hypothesis. The research further includes a techno-economic study to assess the viability of sources’ placement at multiple positions within the mesh network, comparing it with the optimal placement scenario involving a single position for all sources. This methodology serves as a valuable resource for system designers and operators aiming to minimize line losses and optimize energy distribution in DC microgrids in a mesh topology.

Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction.

Massive penetration of plug-in electric vehicles (EVs) may create challenges in the near future for the distribution network. Moreover, this may lead to an increase of the transformers' aging rate and a reduction of the financial profits. In this paper, a novel approach is proposed, in which the operational margin of the transformer is optimized based on the transformer's internal characteristics, its loss of life, and the variation of the ambient temperature. This operational power limit should not be exceeded to guarantee that the loss of life of the transformer is equal to or less than the one provided by the manufacturer. For validation purposes, a comparative study between the conventional method and the suggested one is presented. This paper is applied to a parking lot for charging EVs, which is supplied by a distribution transformer. In contrary to the conventional method, the one suggested in this paper can guarantee a predefined transformer loss of life. Simulation results show that the proposed method increases the transformer lifetime, reduces the loss of life, and reduces its depreciation cost by 63% in certain conditions. In addition, it increases the financial profit for the parking lot's owner up to 10% during cold weather.

Different types of Demand Response Programmes (DRPs) exist and can be simultaneously offered by the electrical utilities through established contracts with customers. Operating simultaneously multiple types of DRPs might lead to undesired results. DRPs might have different responses to objectives, time-based ones tend to maximise consumption during lowest tariffs periods while incentive-based ones tend to reduce the usage based on peak events, accordingly contradiction might occur. Thus, synchronising these DRPs and their parameters through an optimised process including customer selection for the appropriate one is a mandatory step. A fair allocation of the various types of DRPs including their execution's priority at a specific time is the main objective of this study. An original approach based on clustering technique for predicting customers' behaviour coupled with a particle swarm optimisation (PSO) to reach an optimal solution for relocation is presented. In this study, an optimal solution is developed; it provides the various DRPs with the most convenient parameters for the best demand/generation balance, utility profit maximisation and operational cost minimisation. The method is validated through a simulation applying a time-based with two incentive-based DRPs in the presence of conventional and renewable generation while using Kmeans clustering and PSO on Matlab.

We consider the problem of localising an unknown number of land mines usingconcentration information provided by a wireless sensor network. A number of vapoursensors/detectors, deployed in the region of interest, are able to detect the concentrationof the explosive vapours, emanating from buried land mines. The collected data iscommunicated to a fusion centre. Using a model for the transport of the explosive chemicalsin the air, we determine the unknown number of sources using a Principal ComponentAnalysis (PCA)-based technique. We also formulate the inverse problem of determiningthe positions and emission rates of the land mines using concentration measurementsprovided by the wireless sensor network. We present a solution for this problem basedon a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme,and we compare it to the least squares optimisation approach. Experiments conducted onsimulated data show the effectiveness of the proposed approach.

In this work, an evaluation and assistance platform for people with disabilities is presented. This platform allows the control of different systems (PC games, robot, electric wheelchair) using different interfaces (mouse, joystick, etc.). The software facilitates the saving of experimental data for analysis, it can be automatically reconfigured to match user’s ability, and it is operated locally or remotely through the internet. Quantitative indicators are defined for evaluation purposes and to determine the needed assistance and adaptation. The system is tested and results are presented and discussed.